Baseline estrogen levels in postmenopausal women participating in the MAP.3 breast cancer chemoprevention trial
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to quantify baseline estradiol (E2) and estrone (E1) concentrations according to selected patient characteristics in a substudy nested within the MAP.3 chemoprevention trial. METHODS: E2 and E1 levels were measured in 4,068 postmenopausal women using liquid chromatography-tandem mass spectrometry. Distributions were described by age, years since menopause, race, body mass index (BMI), smoking status, and use and duration of hormone therapy using the Kruskal-Wallis test. Multivariable linear regression was also used to identify characteristics associated with estrogen levels. RESULTS: After truncation at the 97.5th percentile, the mean (SD)/median (IQR) values for E2 and E1 were 5.41 (4.67)/4.0 (2.4-6.7) pg/mL and 24.7 (14.1)/21 (15-31) pg/mL, respectively. E2 and E1 were strongly correlated (Pearson correlation [r] = 0.8, P < 0.01). The largest variation in E2 and E1 levels was by BMI; mean E2 and E1 levels were 3.5 and 19.1 pg/mL, respectively for women with BMI less than 25 and 7.5 and 30.6 pg/mL, respectively, for women with BMI greater than 30. E2 and E1 varied by age, BMI, smoking status, and prior hormone therapy in multivariable models (P < 0.01). CONCLUSIONS: There was large interindividual variability observed for E2 and E1 that varied significantly by participant characteristics, but with small absolute differences except in the case of BMI. Although the majority of participant characteristics were independently associated with E1 and E2, together, these factors only explained about 20% of the variation in E1 and E2 levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".